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Neural Style Transfer: A Review.

Yongcheng Jing, Yezhou Yang, Zunlei Feng

    IEEE Transactions on Visualization and Computer Graphics
    |June 11, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This review offers a comprehensive overview of Neural Style Transfer (NST), a technique using Convolutional Neural Networks (CNNs) for artistic image creation. It categorizes NST algorithms, evaluates their performance, and discusses future research directions.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Convolutional Neural Networks (CNNs) enable separating and recombining image content and style.
    • Neural Style Transfer (NST) leverages CNNs for artistic image generation, merging content and style.
    • NST has gained significant traction in academic and industrial fields, spurring diverse algorithmic advancements.

    Purpose of the Study:

    • To provide a comprehensive review of the current progress in Neural Style Transfer (NST).
    • To propose a taxonomy for existing NST algorithms.
    • To evaluate and compare different NST approaches and identify future research avenues.

    Main Methods:

    • Categorization of current NST algorithms into a structured taxonomy.
    • Qualitative and quantitative evaluation of various NST methods.
    • Comparative analysis of different NST algorithms based on established metrics.

    Main Results:

    • A structured taxonomy of NST algorithms is presented.
    • Comparative analysis highlights the strengths and weaknesses of different NST approaches.
    • Identification of key applications and open challenges in the field.

    Conclusions:

    • NST is a rapidly evolving field with numerous applications.
    • The proposed taxonomy and evaluation provide a framework for understanding NST progress.
    • Future research should focus on addressing identified open problems and further enhancing NST capabilities.